The present invention is related to the following concurrently filed applications: Model-Based Admission Control Adjustment in Data Networks, by Z. Dziong; Learning-Based Admission Control Adjustment in Data Networks, by Z. Dziong and M. Ji. Each of these concurrently filed applications is assigned to the assignee of the present invention, and each is hereby incorporated by reference into the present application.
The present invention relates to high-speed data networks, such as Asynchronous Transfer Mode (ATM) networks. More particularly, the present invention relates to Admission Control for bandwidth management and congestion control in such networks.
Still more particularly, the present invention relates to the use of Connection Admission Control (CAC) adjustments in ATM networks using network measurement data to further control and tune an analytical CAC system.
In broadband integrated services networks, e.g., those using asynchronous transfer mode (ATM) systems and techniques, information is packetized in fixed length xe2x80x9ccellsxe2x80x9d for statistical multiplexing with other traffic for transmission over high-bit-rate channels. Such networks are connection oriented, so a connection must be established before transmission begins. Moreover, these connections are usually subject to contracts between a network operator and users of the network. To ensure quality of service (QoS) consistent with these contracts, connection admission control (CAC) techniques are typically employed in management of such networks. Generally, CAC algorithms determine whether a new virtual channel connection should be admitted to the network based on network statusxe2x80x94such as available resources, cell loss performancexe2x80x94and contract parameters (e.g., mean traffic rate and peak traffic rate). See generally, Dziong, Z., ATM Network Resource Management, McGraw-Hill, 1997.
Because of the complex variety of connection types and services, and consequent difficulty in ascertaining complete and current information regarding the actual state of ATM networks, and because of possible adverse consequences of failing to honor QoS guarantees in customer contracts, many network operators have chosen to use CAC algorithms that are quite conservative. Most CAC algorithms are designed for worst-case source behavior. Moreover, analytical models applied in these algorithms are also conservativexe2x80x94to account for the difficulty in achieving exact modeling of the connection aggregate process. Such conservative approaches in many cases tend to offset statistical multiplexing gains and other system efficiencies available in ATM networks.
Some have proposed using actual network measurements (such as traffic level and cell-loss characteristics in light of corresponding QoS constraints) to adjust CAC mechanisms in an attempt to more fully use network resources. See, for example, Bensaou, B.; Lam, S. T. C.; Chu, H. and Tsang, D. H. K., xe2x80x9cEstimation of the Cell Loss Ratio in ATM Networks with a Fuzzy System and Application to Measurement-Based Call Admission Control,xe2x80x9d IEEE/ACM Transactions on Networking, VOL. 5, NO. 4 (August 1997), pp. 572-584; Gibbens, R. J., Kelly, F. P., and Key, P. B., xe2x80x9cA decision-theoretic approach to call admission control in ATM networks,xe2x80x9d IEEE Journal on Selected Areas in Communication, 13(6):1101-1114 (1995); and Saito, H. xe2x80x9cDynamic call admission control in ATM networks, IEEE Journal on selected Areas in Communication, 9(7):982-989 (1991).
Thus far however, attempts to use network operating measurements have proven difficult in network administration, especially in respect of their incorporation in CAC processes. A particular difficulty arises in some prior art CAC processes in efficiently treating operations in networks exhibiting a wide variety of traffic types with a concomitant variety of QoS constraints. High bandwidth efficiencies through CAC tuning have not been readily available without high precision measurements.
The present invention overcomes limitations of prior art CAC algorithms and achieves a technical advance, as described in connection with illustrative embodiments presented below.
In accordance with one aspect of the present invention, the concept of aggregate effective bandwidth, AEBW, is used to provide a useful approximation to required bandwidth for given levels and classes of network traffic. AEBW is used in deriving an allowed level of overbookingxe2x80x94expressed in terms of an overbooking gain, xcex1t.
In accordance with another aspect of the present invention, an estimate is made of the probability distribution function for a maximum equivalent bandwidth, typically in the context of a network operations system, to provide information to switches to control over-booking gain. This over-booking gain is derived from a count of cells arriving in periodic time slots and is updated from time to time based on measurement values sent from individual switches to the operations system. Time slots duration for the counts is illustratively chosen in accordance with buffer length at ports of the switches.
In illustrative operation, a maximum of counts determined during a representative 15-minute interval and is sent to the operations system. Only the maximum count need be sent. From the individual measurements, the operations system estimates the probability distribution function by histograms of the measured values.
The presently disclosed embodiments provide tools that can be used for tuning call/traffic admission control and for network bandwidth management and dimensioning purposes. While the proposed methods and systems are described in the context of ATM network links, embodiments of the present invention are applicable to any packet-switched network.